A variety of technologies exist for collecting and mining user activity data reflective of the actions and preferences of users of an electronic catalog. For example, it is known in the art to collectively analyze the activity data of a population of users to identify items that tend to be viewed, purchased, or otherwise selected in combination. Different types of item relationships may be detected by applying different similarity algorithms and metrics to the activity data. For instance, a pair of items, A and B, may be identified as likely substitutes on the basis that a relatively large number of the users who view A also view B during the same browsing session. Items C and D, on the other hand, may be identified as complementary because a relatively large number of those who purchase C also purchase D.
The item relationships extracted from the user activity data may be exposed to users of the electronic catalog to assist users in identifying items of interest. For example, in some systems, when a user views a catalog item, the user is informed of other items that are commonly viewed (or purchased) by those who have viewed (or purchased) the item. Although this type of data is helpful, users could benefit from knowing more about the relationships that exist between specific items.